def __init__(self, inputx, indices, updates):
super(TestScatterAddDynamicNet, self).__init__()
self.scatter_add = P.ScatterAdd()
self.test_dynamic = inner.GpuConvertToDynamicShape()
self.inputx = Parameter(inputx, name="inputx")
self.indices = Parameter(indices, name="indices")
self.updates = Parameter(updates, name="updates")
def __init__(self, axis=0, dyn_a=True, dyn_b=True):
super(GatherNetDynamic, self).__init__()
self.gather = P.Gather()
self.gpu_convert_to_dynamic_shape = inner.GpuConvertToDynamicShape()
self.to_dyn_1 = dyn_a
self.to_dyn_2 = dyn_b
self.axis = axis
def __init__(self, num_segments, dyn_a=True, dyn_b=True):
super(UnsortedSegmentMaxDynNet, self).__init__()
self.unsorted_segment_max = P.UnsortedSegmentMax()
self.gpu_convert_to_dynamic_shape = inner.GpuConvertToDynamicShape()
self.num_segments = num_segments
self.to_dyn_1 = dyn_a
self.to_dyn_2 = dyn_b
def __init__(self, input_1, input_2, perm_1, perm_2):
super(Transpose_dynamic2, self).__init__()
self.transpose = P.Transpose()
self.test_dynamic = inner.GpuConvertToDynamicShape()
self.x_1 = input_1
self.x_2 = input_2
self.perm_1 = perm_1
self.perm_2 = perm_2
def __init__(self, nptype):
super(Transpose_dynamic, self).__init__()
self.transpose = P.Transpose()
self.test_dynamic = inner.GpuConvertToDynamicShape()
self.x = Parameter(
initializer(Tensor(np.arange(1 * 2 * 3 * 4 * 5).reshape(1, 2, 3, 4, 5).astype(nptype)),
[1, 2, 3, 4, 5]), name='5D')
self.perm = (1, 0, 3, 4, 2)
def __init__(self, axis=0, out_nums=1):
super(NetConv2dDynamic, self).__init__()
self.dynshape = inner.GpuConvertToDynamicShape()
out_channel = 2
kernel_size = 1
self.conv = P.Conv2D(out_channel,
kernel_size,
mode=1,
pad_mode="valid",
pad=0,
stride=1,
dilation=1,
group=1)
def __init__(self,
num_features,
gamma_init,
beta_init,
mean_init,
var_init,
use_batch_statistics=None):
super(NetFusedBatchNormExDynamic, self).__init__()
self.bn = P.FusedBatchNormEx(mode=1, epsilon=0.00001, momentum=0.1)
self.moving_mean = Parameter(initializer(mean_init, num_features),
name="mean",
requires_grad=False)
self.moving_variance = Parameter(initializer(var_init, num_features),
name="variance",
requires_grad=False)
self.gamma = Parameter(initializer(gamma_init, num_features),
name="gamma",
requires_grad=True)
self.beta = Parameter(initializer(beta_init, num_features),
name="beta",
requires_grad=True)
self.dynshape = inner.GpuConvertToDynamicShape()
def __init__(self):
super(ReduceAllDynamic, self).__init__()
self.reduceall = P.ReduceAll(False)
self.test_dynamic = inner.GpuConvertToDynamicShape()
def __init__(self):
super(TestScatterAddDynamicNet2, self).__init__()
self.scatter_add = P.ScatterAdd()
self.test_dynamic = inner.GpuConvertToDynamicShape()
def __init__(self):
super(Tensoradd_d, self).__init__()
self.test_dynamic = inner.GpuConvertToDynamicShape()
self.add = P.Add()
def __init__(self):
super(NetMul_dynamic, self).__init__()
self.mul = P.Mul()
self.test_dynamic = inner.GpuConvertToDynamicShape()
def __init__(self):
super(DynamicNet, self).__init__()
self.HSigmoid = P.HSigmoid()
self.d = inner.GpuConvertToDynamicShape()
def __init__(self):
super(ReduceMinDynamic, self).__init__()
self.reducemin = P.ReduceMin(False)
self.test_dynamic = inner.GpuConvertToDynamicShape()
def __init__(self):
super(GpuConvertToDynamicShapeNet, self).__init__()
self.gpu_convert_to_dynamic_shape = inner.GpuConvertToDynamicShape(
)
def __init__(self):
super(SqaureNetDynamic, self).__init__()
self.square = P.Square()
self.gpu_convert_to_dynamic_shape = inner.GpuConvertToDynamicShape()
def __init__(self):
super(NetReluDynamic, self).__init__()
self.conv = inner.GpuConvertToDynamicShape()
self.relu = P.ReLU()
def __init__(self):
super(ReduceSumDynamic, self).__init__()
self.reducesum = P.ReduceSum(True)
self.test_dynamic = inner.GpuConvertToDynamicShape()
def __init__(self):
super(SequenceMaskDynamicNet2, self).__init__()
self.convert_to_dynamic_shape = inner.GpuConvertToDynamicShape()
def __init__(self, x, axis):
super(ReduceAnyDynamic, self).__init__()
self.reduceany = P.ReduceAny(False)
self.test_dynamic = inner.GpuConvertToDynamicShape()
self.x = x
self.axis = axis
def __init__(self, transpose_a=False, transpose_b=False):
super(BatchMatMul_d, self).__init__()
self.batch_matmul = P.BatchMatMul(transpose_a, transpose_b)
self.test_dynamic = inner.GpuConvertToDynamicShape()
def __init__(self, inputx):
super(TestScatterUpdateDynamicNet2, self).__init__()
self.scatter_update = P.ScatterUpdate()
self.test_dynamic = inner.GpuConvertToDynamicShape()
self.inputx = Parameter(inputx, name="inputx")
def __init__(self):
super(NetDynamic, self).__init__()
self.conv = inner.GpuConvertToDynamicShape()
self.expand_dims = P.ExpandDims()
def __init__(self):
super(ZerosLikeDynamicNet, self).__init__()
self.gpu_convert_to_dynamic_shape = inner.GpuConvertToDynamicShape()
self.zeros_like = P.ZerosLike()
def __init__(self, axis=0, out_nums=1):
super(NetDynamic, self).__init__()
self.conv = inner.GpuConvertToDynamicShape()
self.split = P.Split(axis, out_nums)
def __init__(self, x, axis, keepdims=False):
super(ReduceMeanDynamic, self).__init__()
self.test_dynamic = inner.GpuConvertToDynamicShape()
self.reducemean = P.ReduceMean(keep_dims=keepdims)
self.x = x
self.axis = axis
def __init__(self, type0, type1):
super(NetDynamic, self).__init__()
self.conv = inner.GpuConvertToDynamicShape()
self.Cast = P.Cast()
self.type0 = type0
self.type1 = type1
def __init__(self):
super(AssertDynamicShapeNet, self).__init__()
self.gpu_convert_to_dynamic_shape = inner.GpuConvertToDynamicShape(
)
self.error_on_dynamic_shape_input = inner.ErrorOnDynamicShapeInput(
)
def __init__(self):
super(NetEqualDynamic, self).__init__()
self.conv = inner.GpuConvertToDynamicShape()
self.Equal = P.Equal()
def __init__(self):
super(BiasAddDynamic, self).__init__()
self.ba = P.BiasAdd()
self.test_dynamic = inner.GpuConvertToDynamicShape()
def __init__(self, maxlen):
super(SequenceMaskDynamicNet, self).__init__()
self.maxlen = maxlen
self.convert_to_dynamic_shape = inner.GpuConvertToDynamicShape()
self.sequence_mask = P.SequenceMask()